Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem-Reference-Cited by-同舟云学术

Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem

Published:2023-12 Issue: Volume: Page:100064
ISSN:2543-9251
Container-title:Data and Information Management
language:en
Short-container-title:Data and Information Management

Author:

Zhou Qian,Sun Bo

Funder

Natural Science Foundation of Shandong Province

Publisher

Elsevier BV

Subject

Library and Information Sciences,Computer Science (miscellaneous)

Reference35 articles.

1. Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework;Alcalá-Fdez;Journal of Multiple-Valued Logic and Soft Computing,2011

2. Random forests;Breiman;Machine Learning,2001

3. Enhancing techniques for learning decision trees from imbalanced data;Chaabane;Advances in Data Analysis and Classification,2020

4. Navo minority over-sampling technique (NMOTe): A consistent performance booster on imbalanced datasets;Chakrabarty;Journal of Electronics and Informatics,2020

5. Smote: Synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the remaining life of oil pipeline circumferential welds based on hybrid machine learning-based methods;Energy;2024-10

2. Integrated Risk Assessment of Mountainous Long-Distance Oil and Gas Pipelines Based on Multisource Spatial Data;ACS Omega;2024-07-02

3. Customer Churn Prediction in the Telecom Sector with Machine Learning and Adaptive k-Means Cluster using Imbalance Data;2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT);2024-03-15